Adversarial Time-to-Event Modeling

Understand

Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice.

• Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statistical models.
• We present a deep-network-based approach that leverages adversarial learning to address a key challenge in modern time-to-event modeling: nonparametric estimation of event-time distributions.
• We also introduce a principled cost function to exploit information from censored events (events that occur subsequent to the observation window).